the lmsys gap between sota and the rest is enormous and just widening
MACHINE LEARNING
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llm.c versus PyTorch: speed and simplicity comparison
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…"in that llm.c will never be as fast nor as simple as pytorch"
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Deep Learning Performance Optimization: Complexity and Resources
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This post became popular; Few more thoughts / pointers on the topic for the interested reader. Example of the complexity involved: @cHHillee has a great post "Making Deep Learning Go Brrrr From First Principles" https://
horace.io/brrr_intro.html
I was always struck by this diagram from -
Computer Vision in Pathology: Future Nobel Prize Potential
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Two decades back, I worked in computer vision applied to Cryo Electron Microscopy. In 2017, the three top people in the field got the Nobel Prize in chemistry. Now we are looking at computational pathology and I see so much potential. I won't be surprised if someone wins the
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Working with Machine Learning Models and Big Data Technologies
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Working with #MachineLearning model
by @DataScienceDojo #BigData #MachineLearning #ArtificialIntelligence #ML #MI #DataScience cc: @pbalakrishnarao @rtehrani @pascal_bornet https://
buff.ly/4aofQMr -
Six Statistical and Machine Learning Pitfalls in Data Science
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6 Statistical and #MachineLearning pitfalls
by @levikul09 #BigData #ArtificialIntelligence #MI #DataScience #DL cc: @pascal_bornet @yvesmulkers @kuriharan -
Amazing Robot Capable of Drawing Hundreds of Objects Autonomously
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This amazing little #Robot can draw hundreds of objects
— Ronald van Loon (@Ronald_vanLoon) 12 avril 2024
via @FrRonconi#Robotics #ArtificialIntelligence #Innovation #Autonomous #Technology #FutureTech
cc: @pascal_bornet @pbalakrishnarao @ravikikan pic.twitter.com/gQDq0M0lYKThis amazing little #Robot can draw hundreds of objects via @FrRonconi #Robotics #ArtificialIntelligence #Innovation #Autonomous #Technology #FutureTech cc: @pascal_bornet @pbalakrishnarao @ravikikan
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Distilling Step-by-Step: Training Smaller LLMs with Reasoning
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Jason Fries discusses "distilling step-by-step." This method prompts an LLM to provide not just an answer, but also the reasoning behind it. This information is then used to train a smaller, model with similar performance. Watch here: https://
youtube.com/watch?v=TIqf4L
MNCjU
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Does scaling solve creative problem solving limitations in AI?
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Do you think that the difficulties with creative problem solving result from insufficient scaling?
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Model Parameters and Training Data Reproduction in AI
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Still, this particular example merely demonstrates that a model with more parameters is better at reproducing the training data